Learning to Transfer Relational Representations through Analogy

We propose a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. We collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. This dataset is adopted to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. The model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a relation extraction task.

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Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification

Sensors ◽

10.3390/s20123603 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3603

Author(s):

Dasol Jeong ◽

Hasil Park ◽

Joongchol Shin ◽

Donggoo Kang ◽

Joonki Paik

Keyword(s):

Large Scale ◽

Body Shape ◽

State Of The Art ◽

The State ◽

Whole Body ◽

Distinctive Features ◽

Common Features ◽

Siamese Network ◽

Art Methods ◽

Triplet Loss

Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. In this paper, we present a novel attentive learning-based Siamese network for person Re-ID. Unlike existing methods, we designed an attention module and attention loss using the properties of the Siamese network to concentrate attention on common and distinctive features. The attention module consists of channel attention to select important channels and encoder-decoder attention to observe the whole body shape. We modified the triplet loss into an attention loss, called uniformity loss. The uniformity loss generates a unique attention map, which focuses on both common and discriminative features. Extensive experiments show that the proposed network compares favorably to the state-of-the-art methods on three large-scale benchmarks including Market-1501, CUHK03 and DukeMTMC-ReID datasets.

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Biomedical Relation Extraction Using Distant Supervision

Scientific Programming ◽

10.1155/2020/8893749 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Nada Boudjellal ◽

Huaping Zhang ◽

Asif Khan ◽

Arshad Ahmad

Keyword(s):

Big Data ◽

Information Extraction ◽

State Of The Art ◽

Relation Extraction ◽

Knowledge Bases ◽

Structured Data ◽

Distant Supervision ◽

Future Challenges ◽

Unstructured Information ◽

Biomedical Relation Extraction

With the accelerating growth of big data, especially in the healthcare area, information extraction is more needed currently than ever, for it can convey unstructured information into an easily interpretable structured data. Relation extraction is the second of the two important tasks of relation extraction. This study presents an overview of relation extraction using distant supervision, providing a generalized architecture of this task based on the state-of-the-art work that proposed this method. Besides, it surveys the methods used in the literature targeting this topic with a description of different knowledge bases used in the process along with the corpora, which can be helpful for beginner practitioners seeking knowledge on this subject. Moreover, the limitations of the proposed approaches and future challenges were highlighted, and possible solutions were proposed.

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Cross-Relation Cross-Bag Attention for Distantly-Supervised Relation Extraction

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.3301419 ◽

2019 ◽

Vol 33 ◽

pp. 419-426 ◽

Cited By ~ 6

Author(s):

Yujin Yuan ◽

Liyuan Liu ◽

Siliang Tang ◽

Zhongfei Zhang ◽

Yueting Zhuang ◽

...

Keyword(s):

Selective Attention ◽

Supervised Learning ◽

State Of The Art ◽

Relation Extraction ◽

Knowledge Bases ◽

Training Data ◽

Distant Supervision ◽

Sentence Level ◽

Noise Robust

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C2SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.

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Joint Extraction of Entities and Relations Based on a Novel Graph Scheme

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/620 ◽

2018 ◽

Cited By ~ 11

Author(s):

Shaolei Wang ◽

Yue Zhang ◽

Wanxiang Che ◽

Ting Liu

Keyword(s):

Directed Graph ◽

State Of The Art ◽

Relation Extraction ◽

The State ◽

The Other ◽

Joint Learning ◽

Joint Decoding ◽

Joint Task ◽

Art Methods ◽

Parameter Sharing

Both entity and relation extraction can benefit from being performed jointly, allowing each task to correct the errors of the other. Most existing neural joint methods extract entities and relations separately and achieve joint learning through parameter sharing, leading to a drawback that information between output entities and relations cannot be fully exploited. In this paper, we convert the joint task into a directed graph by designing a novel graph scheme and propose a transition-based approach to generate the directed graph incrementally, which can achieve joint learning through joint decoding. Our method can model underlying dependencies not only between entities and relations, but also between relations. Experiments on NewYork Times (NYT) corpora show that our approach outperforms the state-of-the-art methods.

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Bias Modeling for Distantly Supervised Relation Extraction

Mathematical Problems in Engineering ◽

10.1155/2015/969053 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Yang Xiang ◽

Yaoyun Zhang ◽

Xiaolong Wang ◽

Yang Qin ◽

Wenying Han

Keyword(s):

Language Processing ◽

Learning Algorithm ◽

State Of The Art ◽

Relation Extraction ◽

Knowledge Bases ◽

Training Data ◽

Free Text ◽

Distant Supervision ◽

Annotation Process ◽

Noise Tolerant

Distant supervision (DS) automatically annotates free text with relation mentions from existing knowledge bases (KBs), providing a way to alleviate the problem of insufficient training data for relation extraction in natural language processing (NLP). However, the heuristic annotation process does not guarantee the correctness of the generated labels, promoting a hot research issue on how to efficiently make use of the noisy training data. In this paper, we model two types of biases to reduce noise: (1)bias-distto model the relative distance between points (instances) and classes (relation centers); (2)bias-rewardto model the possibility of each heuristically generated label being incorrect. Based on the biases, we propose three noise tolerant models:MIML-dist,MIML-dist-classify, andMIML-reward, building on top of a state-of-the-art distantly supervised learning algorithm. Experimental evaluations compared with three landmark methods on the KBP dataset validate the effectiveness of the proposed methods.

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Density Guarantee on Finding Multiple Subgraphs and Subtensors

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3446668 ◽

2021 ◽

Vol 15 (5) ◽

pp. 1-32

Author(s):

Quang-huy Duong ◽

Heri Ramampiaro ◽

Kjetil Nørvåg ◽

Thu-lan Dam

Keyword(s):

Lower Bound ◽

State Of The Art ◽

The State ◽

The Other ◽

Exact Methods ◽

Practical Solution ◽

Novel Approach ◽

Wide Range ◽

Real World Datasets ◽

Tensor Data

Dense subregion (subgraph & subtensor) detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Approximation approaches are commonly used for detecting dense subregions due to the complexity of the exact methods. Existing algorithms are generally efficient for dense subtensor and subgraph detection, and can perform well in many applications. However, most of the existing works utilize the state-or-the-art greedy 2-approximation algorithm to capably provide solutions with a loose theoretical density guarantee. The main drawback of most of these algorithms is that they can estimate only one subtensor, or subgraph, at a time, with a low guarantee on its density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data and giving a higher lower bound of the density. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subgraph and subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real-world datasets, which demonstrates its efficiency and feasibility.

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Named Entity Recognition and Relation Extraction

ACM Computing Surveys ◽

10.1145/3445965 ◽

2021 ◽

Vol 54 (1) ◽

pp. 1-39

Author(s):

Zara Nasar ◽

Syed Waqar Jaffry ◽

Muhammad Kamran Malik

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Named Entity Recognition ◽

Relation Extraction ◽

The State ◽

Entity Recognition ◽

Joint Models ◽

Named Entity ◽

Textual Data ◽

Benchmark Datasets

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

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Smoothing vs. sharpening of colour images: Together or separated

Applied Mathematics and Nonlinear Sciences ◽

10.21042/amns.2017.1.00025 ◽

2017 ◽

Vol 2 (1) ◽

pp. 299-316 ◽

Cited By ~ 8

Author(s):

Cristina Pérez-Benito ◽

Samuel Morillas ◽

Cristina Jordán ◽

J. Alberto Conejero

Keyword(s):

Image Denoising ◽

Gaussian Distribution ◽

State Of The Art ◽

Visual Quality ◽

The State ◽

Art Methods ◽

Efficiency And Effectiveness

AbstractIt is still a challenge to improve the efficiency and effectiveness of image denoising and enhancement methods. There exists denoising and enhancement methods that are able to improve visual quality of images. This is usually obtained by removing noise while sharpening details and improving edges contrast. Smoothing refers to the case of denoising when noise follows a Gaussian distribution.Both operations, smoothing noise and sharpening, have an opposite nature. Therefore, there are few approaches that simultaneously respond to both goals. We will review these methods and we will also provide a detailed study of the state-of-the-art methods that attack both problems in colour images, separately.

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Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

Prague Bulletin of Mathematical Linguistics ◽

10.1515/pralin-2017-0029 ◽

2017 ◽

Vol 108 (1) ◽

pp. 307-318 ◽

Cited By ~ 1

Author(s):

Eleftherios Avramidis

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Machine Translation ◽

State Of The Art ◽

Linear Method ◽

The State ◽

Quality Estimation ◽

Art Methods ◽

Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

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